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Artificial Neural Nets and the Representation of Human Concepts (2312.05337v2)
Published 8 Dec 2023 in cs.LG and cs.AI
Abstract: What do artificial neural networks (ANNs) learn? The ML community shares the narrative that ANNs must develop abstract human concepts to perform complex tasks. Some go even further and believe that these concepts are stored in individual units of the network. Based on current research, I systematically investigate the assumptions underlying this narrative. I conclude that ANNs are indeed capable of performing complex prediction tasks, and that they may learn human and non-human concepts to do so. However, evidence indicates that ANNs do not represent these concepts in individual units.
- From attribution maps to human-understandable explanations through concept relevance propagation. Nature Machine Intelligence 5(9): 1006–1019 . Andrews and Beck (2017) Andrews, K. and J. Beck. 2017. The Routledge handbook of philosophy of animal minds. Taylor & Francis. Ballet et al. (2019) Ballet, V., X. Renard, J. Aigrain, T. Laugel, P. Frossard, and M. Detyniecki. 2019. Imperceptible adversarial attacks on tabular data. arXiv preprint arXiv:1911.03274 . Bau et al. (2017) Bau, D., B. Zhou, A. Khosla, A. Oliva, and A. Torralba 2017. Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6541–6549. Bau et al. (2018) Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Andrews, K. and J. Beck. 2017. The Routledge handbook of philosophy of animal minds. Taylor & Francis. Ballet et al. (2019) Ballet, V., X. Renard, J. Aigrain, T. Laugel, P. Frossard, and M. Detyniecki. 2019. Imperceptible adversarial attacks on tabular data. arXiv preprint arXiv:1911.03274 . Bau et al. (2017) Bau, D., B. Zhou, A. Khosla, A. Oliva, and A. Torralba 2017. Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6541–6549. Bau et al. (2018) Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ballet, V., X. Renard, J. Aigrain, T. Laugel, P. Frossard, and M. Detyniecki. 2019. Imperceptible adversarial attacks on tabular data. arXiv preprint arXiv:1911.03274 . Bau et al. (2017) Bau, D., B. Zhou, A. Khosla, A. Oliva, and A. Torralba 2017. Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6541–6549. Bau et al. (2018) Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bau, D., B. Zhou, A. Khosla, A. Oliva, and A. Torralba 2017. Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6541–6549. Bau et al. (2018) Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- The Routledge handbook of philosophy of animal minds. Taylor & Francis. Ballet et al. (2019) Ballet, V., X. Renard, J. Aigrain, T. Laugel, P. Frossard, and M. Detyniecki. 2019. Imperceptible adversarial attacks on tabular data. arXiv preprint arXiv:1911.03274 . Bau et al. (2017) Bau, D., B. Zhou, A. Khosla, A. Oliva, and A. Torralba 2017. Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6541–6549. Bau et al. (2018) Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ballet, V., X. Renard, J. Aigrain, T. Laugel, P. Frossard, and M. Detyniecki. 2019. Imperceptible adversarial attacks on tabular data. arXiv preprint arXiv:1911.03274 . Bau et al. (2017) Bau, D., B. Zhou, A. Khosla, A. Oliva, and A. Torralba 2017. Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6541–6549. Bau et al. (2018) Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bau, D., B. Zhou, A. Khosla, A. Oliva, and A. Torralba 2017. Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6541–6549. Bau et al. (2018) Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Imperceptible adversarial attacks on tabular data. arXiv preprint arXiv:1911.03274 . Bau et al. (2017) Bau, D., B. Zhou, A. Khosla, A. Oliva, and A. Torralba 2017. Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6541–6549. Bau et al. (2018) Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bau, D., B. Zhou, A. Khosla, A. Oliva, and A. Torralba 2017. Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6541–6549. Bau et al. (2018) Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Network dissection: Quantifying interpretability of deep visual representations. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6541–6549. Bau et al. (2018) Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bau, D., J.Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman, and A. Torralba 2018. Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Gan dissection: Visualizing and understanding generative adversarial networks. In International Conference on Learning Representations. Belkin (2021) Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Belkin, M. 2021. Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numerica 30: 203–248 . Belkin et al. (2019) Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Belkin, M., D. Hsu, S. Ma, and S. Mandal. 2019. Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences 116(32): 15849–15854 . Bird and Tobin (2023) Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bird, A. and E. Tobin. 2023. Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Natural Kinds, In The Stanford Encyclopedia of Philosophy (Spring 2023 ed.)., eds. Zalta, E.N. and U. Nodelman. Metaphysics Research Lab, Stanford University. Bowers et al. (2022) Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Bowers, J.S., G. Malhotra, M. Dujmović, M.L. Montero, C. Tsvetkov, V. Biscione, G. Puebla, F. Adolfi, J.E. Hummel, R.F. Heaton, et al. 2022. Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Deep problems with neural network models of human vision. Behavioral and Brain Sciences: 1–74 . Brown et al. (2017) Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Brown, T.B., D. Mané, A. Roy, M. Abadi, and J. Gilmer. 2017. Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Adversarial patch. arXiv preprint arXiv:1712.09665 . Buckner (2018) Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Buckner, C. 2018. Empiricism without magic: Transformational abstraction in deep convolutional neural networks. Synthese 195(12): 5339–5372 . Buckner (2020) Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Buckner, C. 2020. Understanding adversarial examples requires a theory of artefacts for deep learning. Nature Machine Intelligence 2(12): 731–736 . Del Pinal (2016) Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Del Pinal, G. 2016. Prototypes as compositional components of concepts. Synthese 193: 2899–2927 . Donnelly and Roegiest (2019) Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Donnelly, J. and A. Roegiest 2019. On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- On interpretability and feature representations: an analysis of the sentiment neuron. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part I 41, pp. 795–802. Springer. Duede (2023) Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Duede, E. 2023. The representational status of deep learning models. arXiv preprint arXiv:2303.12032 . Erickson et al. (2017) Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Erickson, B.J., P. Korfiatis, Z. Akkus, and T.L. Kline. 2017. Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Machine learning for medical imaging. Radiographics 37(2): 505–515 . Freiesleben (2022) Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Freiesleben, T. 2022. The intriguing relation between counterfactual explanations and adversarial examples. Minds and Machines 32(1): 77–109 . Freiesleben and Grote (2023) Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T. and T. Grote. 2023. Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Beyond generalization: a theory of robustness in machine learning. Synthese 202(4): 109 . Freiesleben et al. (2022) Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Freiesleben, T., G. König, C. Molnar, and A. Tejero-Cantero. 2022. Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Scientific inference with interpretable machine learning: Analyzing models to learn about real-world phenomena. arXiv preprint arXiv:2206.05487 . Frigg and Nguyen (2021) Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Frigg, R. and J. Nguyen. 2021. Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Scientific Representation, In The Stanford Encyclopedia of Philosophy (Winter 2021 ed.)., ed. Zalta, E.N. Metaphysics Research Lab, Stanford University. Gao et al. (2020) Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gao, Y., G.Y. Cai, W. Fang, H.Y. Li, S.Y. Wang, L. Chen, Y. Yu, D. Liu, S. Xu, P.F. Cui, et al. 2020. Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Machine learning based early warning system enables accurate mortality risk prediction for covid-19. Nature communications 11(1): 5033 . Geirhos et al. (2020) Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., J.H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F.A. Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Shortcut learning in deep neural networks. Nature Machine Intelligence 2(11): 665–673 . Geirhos et al. (2018) Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Geirhos, R., P. Rubisch, C. Michaelis, M. Bethge, F.A. Wichmann, and W. Brendel 2018. Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In International Conference on Learning Representations. Goodfellow et al. (2016) Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Deep learning. MIT press. Goodfellow et al. (2014) Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Goodfellow, I.J., J. Shlens, and C. Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 . Grujicic (2023) Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Grujicic, B. 2023. Deep convolutional neural networks are not mechanistic explanations of object recognition. Gurnee and Tegmark (2023) Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Gurnee, W. and M. Tegmark. 2023. Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Language models represent space and time. arXiv preprint arXiv:2310.02207 . Hampton (2006) Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Hampton, J.A. 2006. Concepts as prototypes. Psychology of learning and motivation 46: 79–113 . Hampton and Jönsson (2012) Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hampton, J.A. and M.L. Jönsson. 2012, 02. 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- 385 Typicality and Composition a Lity: the Logic of Combining Vague Concepts, The Oxford Handbook of Compositionality. Oxford University Press. 10.1093/oxfordhb/9780199541072.013.0018. Hastie et al. (2009) Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- The elements of statistical learning: data mining, inference, and prediction, Volume 2. Springer. Hornik et al. (1989) Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Multilayer feedforward networks are universal approximators. Neural networks 2(5): 359–366 . Ilyas et al. (2019) Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ilyas, A., S. Santurkar, D. Tsipras, L. Engstrom, B. Tran, and A. Madry. 2019. Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Adversarial examples are not bugs, they are features. Advances in Neural Information Processing Systems 32 . Keil (1992) Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Keil, F.C. 1992. Concepts, kinds, and cognitive development. MIT Press. Kim et al. (2018) Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, B., M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, et al. 2018. Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In International conference on machine learning, pp. 2668–2677. PMLR. Kim et al. (2021) Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kim, E., J. Lee, and J. Choo 2021. Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Biaswap: Removing dataset bias with bias-tailored swapping augmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 14992–15001. Kingma and Ba (2014) Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kingma, D.P. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 . Koh et al. (2020) Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Koh, P.W., T. Nguyen, Y.S. Tang, S. Mussmann, E. Pierson, B. Kim, and P. Liang 2020. Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Concept bottleneck models. In International conference on machine learning, pp. 5338–5348. PMLR. König et al. (2023) König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . König, G., T. Freiesleben, and M. Grosse-Wentrup 2023. Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Improvement-focused causal recourse (icr). In Proceedings of the AAAI Conference on Artificial Intelligence, Volume 37, pp. 11847–11855. Kornblith et al. (2019) Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Kornblith, S., J. Shlens, and Q.V. Le 2019. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2661–2671. Lalumera (2010) Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Lalumera, E. 2010. Concepts are a functional kind. Behavioral and Brain Sciences 33(2-3): 217 . Leavitt and Morcos (2020a) Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A. Morcos. 2020a. Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Towards falsifiable interpretability research. arXiv preprint arXiv:2010.12016 . Leavitt and Morcos (2020b) Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Leavitt, M.L. and A.S. Morcos. 2020b. On the relationship between class selectivity, dimensionality, and robustness. arXiv preprint arXiv:2007.04440 . Leavitt and Morcos (2020c) Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Leavitt, M.L. and A.S. Morcos 2020c. Selectivity considered harmful: evaluating the causal impact of class selectivity in dnns. In International Conference on Learning Representations. LeCun et al. (2015) LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Deep learning. nature 521(7553): 436–444 . Lipton (2018) Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Lipton, Z.C. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3): 31–57 . Lu et al. (2017) Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Lu, Z., H. Pu, F. Wang, Z. Hu, and L. Wang. 2017. The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- The expressive power of neural networks: A view from the width. Advances in neural information processing systems 30 . Marblestone et al. (2016) Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Marblestone, A.H., G. Wayne, and K.P. Kording. 2016. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience 10: 94 . McKenna et al. (2023) McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . McKenna, N., T. Li, L. Cheng, M.J. Hosseini, M. Johnson, and M. Steedman. 2023. Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Sources of hallucination by large language models on inference tasks. arXiv preprint arXiv:2305.14552 . Meng et al. (2022) Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Meng, K., D. Bau, A. Andonian, and Y. Belinkov. 2022. Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Locating and editing factual associations in gpt. Advances in Neural Information Processing Systems 35: 17359–17372 . Morcos et al. (2018) Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Morcos, A.S., D.G. Barrett, N.C. Rabinowitz, and M. Botvinick 2018. On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- On the importance of single directions for generalization. In International Conference on Learning Representations. Mukhlif et al. (2023) Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Mukhlif, A.A., B. Al-Khateeb, and M.A. Mohammed. 2023. Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2): 570 . Oikarinen et al. (2019) Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Oikarinen, T., K. Srinivasan, O. Meisner, J.B. Hyman, S. Parmar, A. Fanucci-Kiss, R. Desimone, R. Landman, and G. Feng. 2019. Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Deep convolutional network for animal sound classification and source attribution using dual audio recordings. The Journal of the Acoustical Society of America 145(2): 654–662 . Olah et al. (2020) Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., N. Cammarata, L. Schubert, G. Goh, M. Petrov, and S. Carter. 2020. Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Zoom in: An introduction to circuits. Distill 5(3): e00024–001 . Olah et al. (2017) Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Olah, C., A. Mordvintsev, and L. Schubert. 2017. Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Feature visualization. Distill 2(11): e7 . Peacocke (1992) Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Peacocke, C. 1992. A study of concepts. MIT Press. Pearl (2019) Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Pearl, J. 2019. The limitations of opaque learning machines. Possible minds 25: 13–19 . Quine (1969) Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Quine, W.V. 1969. Ontological relativity and other essays. Columbia University Press. Radford et al. (2017) Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Radford, A., R. Jozefowicz, and I. Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 . Räz (2023) Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Räz, T. 2023. Methods for identifying emergent concepts in deep neural networks. Patterns 4(6) . Ren et al. (2021) Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ren, X., X. Li, K. Ren, J. Song, Z. Xu, K. Deng, and X. Wang. 2021. Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Deep learning-based weather prediction: a survey. Big Data Research 23: 100178 . Ribeiro et al. (2016) Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Ribeiro, M.T., S. Singh, and C. Guestrin 2016. ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- ”why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144. Rolnick and Tegmark (2018) Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Rolnick, D. and M. Tegmark 2018. The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- The power of deeper networks for expressing natural functions. In International Conference on Learning Representations. Schölkopf et al. (2021) Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Schölkopf, B., F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Toward causal representation learning. Proceedings of the IEEE 109(5): 612–634 . Shermin et al. (2019) Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Shermin, T., S.W. Teng, M. Murshed, G. Lu, F. Sohel, and M. Paul 2019. Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Enhanced transfer learning with imagenet trained classification layer. In Image and Video Technology: 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18–22, 2019, Proceedings 9, pp. 142–155. Springer. Sterkenburg (2023) Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Sterkenburg, T.F. 2023. Statistical learning theory and occam’s razor: The argument from empirical risk minimization. Szegedy et al. (2013) Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 . Tan et al. (2018) Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Tan, C., F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu 2018. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III 27, pp. 270–279. Springer. Vapnik (1999) Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Vapnik, V. 1999. The nature of statistical learning theory. Springer science & business media. Vaswani et al. (2017) Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Attention is all you need. Advances in neural information processing systems 30 . Wang and Gong (2022) Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wang, S. and Y. Gong. 2022. Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Adversarial example detection based on saliency map features. Applied Intelligence: 1–14 . Watson (2023) Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Watson, D.S. 2023. On the philosophy of unsupervised learning. Philosophy & Technology 36(2): 28 . Wittgenstein (2010) Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Wittgenstein, L. 2010. Philosophical investigations. John Wiley & Sons. Yang et al. (2020) Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yang, J., S. Li, Z. Wang, H. Dong, J. Wang, and S. Tang. 2020. Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges. Materials 13(24): 5755 . Yee (2019) Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Yee, E. 2019. Abstraction and concepts: when, how, where, what and why? Language, Cognition and Neuroscience 34(10): 1257–1265 . Zhang et al. (2021) Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. 2021. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Understanding deep learning (still) requires rethinking generalization. Communications of the ACM 64(3): 107–115 . Zhang et al. (2019) Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhang, S., L. Yao, A. Sun, and Y. Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52(1): 1–38 . Zhong et al. (2023) Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhong, Q., L. Ding, J. Liu, B. Du, and D. Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint arXiv:2302.10198 . Zhou et al. (2018) Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 . Zhou, B., Y. Sun, D. Bau, and A. Torralba. 2018. Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
- Revisiting the importance of individual units in cnns via ablation. arXiv preprint arXiv:1806.02891 .
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